Join us as we shape the next technical directions for 6G. We envision 6G as the result of disruptive changes at three different levels: the network structure, the underlying technologies and the use cases.
An enhanced infrastructure is being developed that incorporates new elements, sensing, and new materials:
- Base stations with expanded capabilities to sense the RF and the physical environment
- Intelligent reflective surfaces
- Aerial base stations -satellite, HAPS or UAV based- operating at high frequencies
New technologies are also being investigated and will likely be ready to become available in the next years to come:
- AI at the PHY and network layers
- Terahertz communication
- Sensor-enabled communication, joint communication and radar
- Metamaterial-based antennas, fluid antennas, and plasmonic arrays
- Hardware-aware signal processing
- Quantum communication and computation
Disruptive use cases will appear as the enabling technology becomes available. We envision some use cases that may dominate the spectrum of cellular applications:
- Immersive extended reality
- Connected robots
- Automated aerial and ground vehicles, transportation
- Connected health
- Precision agriculture
Our team is publishing results of their cutting edge research in the areas of antennas, circuits, signal processing and networking that support the required advance of technology. Some recent publications organized in different working lines are included below:
Hardware and algorithms enabling more antennas, larger bandwidths, and higher carrier frequencies
Y. -S. Yeh and B. A. Floyd, “Multibeam Phased-Arrays Using Dual-Vector Distributed Beamforming: Architecture Overview and 28 GHz Transceiver Prototypes,” in IEEE Transactions on Circuits and Systems I: Regular Papers.
C. Wilson, J. Dean and B. A. Floyd, “Mixer-First MIMO Receiver With Reconfigurable Multi-Port Decoupling and Matching,” in IEEE Journal of Solid-State Circuits, vol. 55, no. 5, pp. 1401-1410, May 2020.
V. T. Bharambe and J. J. Adams, “Planar 2D Beam Steering Antenna Using Liquid Metal Parasitics,” in IEEE Transactions on Antennas and Propagation.
N. Nguyen, K. Lee, and H. Dai, “QR-decomposition-aided Tabu Search Detection for Large MIMO Systems,” IEEE Trans. Vehicular Technology, vol. 68, no. 5, pp. 4857-4870, May 2019.
H. Xie and N. González-Prelcic, “Dictionary Learning for Channel Estimation in Hybrid Frequency-Selective mmWave MIMO Systems,” IEEE Transactions on Wireless Communications, 2020.
N. González-Prelcic, H. Xie, J. Palacios and T. Shimizu, Wideband Channel Tracking and Hybrid Precoding for mmWave MIMO Systems, IEEE Transactions on Wireless Communications, 2020.
N. J. Myers, A. Mezghani and R. W. Heath, “FALP: Fast Beam Alignment in mmWave Systems With Low-Resolution Phase Shifters,” in IEEE Transactions on Communications, vol. 67, no. 12, pp. 8739-8753, Dec. 2019.
Z. Ali, A. Duel-Hallen and H. Hallen, “Early Warning of mmWave Signal Blockage and AoA Transition Using sub-6 GHz Observations,” in IEEE Communications Letters, vol. 24, no. 1, pp. 207-211, Jan. 2020.
D. Baron, C. Rush and Y. Yapici, “mmWave Channel Estimation via Approximate Message Passing with Side Information,” 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, 2020, p
Machine learning and artificial intelligence for communications
S. Hosseinalipour, C. G. Brinton, V. Aggarwal, H. Dai, and M. Chiang, “From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks,” IEEE Communications Magazine, to appear.
Yuyang Wang, N. J. Myers, N. Gonzalez Prelcic, R. W. Heath, Jr., “Site-specific online compressive beam codebook learning in mmWave vehicular communication,” submitted to IEEE Trans. on Wireless, June 2020. [provide arXiv link]
N. J. Myers, Y. Wang, N. González-Prelcic and R. W. Heath, “Deep Learning-Based Beam Alignment in Mmwave Vehicular Networks,” Proc. of ICASSP 2020, Barcelona, Spain, 2020, pp. 8569-8573.
A. F. Dizche, A. Chakrabortty and A. Duel-Hallen, “Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning,” 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 2867-2872.
A Klautau, N González-Prelcic, RW Heath, “LIDAR data for deep learning-based mmWave beam-selection,” IEEE Wireless Communications Letters 8 (3), 909-912, 2019.
M. M. U. Chowdhury, W. Saad and I. Güvenç, “Mobility Management for Cellular-Connected UAVs: A Learning-Based Approach,” 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 2020, pp. 1-6.
X. Lu, L. Xiao, C. Dai and H. Dai, “UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming,” in IEEE Wireless Communications, vol. 27, no. 4, pp. 48-53, August 2020
Radar sensing, communications, and positioning
A. Ali, N GonzalezPrelcic, A Ghosh, “Passive radar at the roadside unit to configure millimeter wave vehicle-to-infrastructure links,” IEEE Transactions on Vehicular Technology, 2020.
A. Ali, N. Gonzalez-Prelcic, R. W. Heath and A. Ghosh, “Leveraging Sensing at the Infrastructure for mmWave Communication,” in IEEE Communications Magazine, vol. 58, no. 7, pp. 84-89, July 2020.
W. Zheng and N. González-Prelcic, “Joint Position, Orientation and Channel Estimation in Hybrid mmWAVE MIMO Systems,” 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2019, pp. 1453-1458.
P. Kumari, A. Mezghani and R. W. Heath, “A MIMO Joint Communication-Radar Measurement Platform at the Millimeter-Wave Band,” 2020 14th European Conference on Antennas and Propagation (EuCAP), Copenhagen, Denmark, 2020, pp. 1-5.
M. Dwyer and D. S. Ricketts, “The North Carolina State University Rabbit Radar: Build a Frequency-Modulated Continuous-Wave Radar in a Day [Application Notes],” in IEEE Microwave Magazine, vol. 21, no. 5, pp. 136-145, May 2020.
T. Fujibayashi, Y. Takeda, W. Wang, Y.-S. Yeh, W. Stapelbroek, S. Takeuchi, and Brian Floyd “A 76- to 81-GHz Multi-Channel Radar Transceiver,” in IEEE Journal of Solid-State Circuits, vol. 52, no. 9, pp. 2226-2241, Sept. 2017.
S. Hosseinalipour, J. Mao, D. Y. Eun and H. Dai, “Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks,” in IEEE Transactions on Network Science and Engineering, vol. 7, no. 3, pp. 1710-1723, 1 July-Sept. 2020.
M. F. Pervej and S.-C. Lin, “Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach,” in Proc. of IEEE VTC2020-Fall, Victoria, B.C., Canada, October 2020.
X. He, R. Jin, and H. Dai, “Physical-Layer Assisted Secure Offloading in Mobile-Edge Computing,” IEEE Trans. Wireless Communications, vol. 19, no. 6, pp. 4054-4066, Jun. 2020.
M. LiWang, S. Dai, Z. Gao, X. Du, M. Guizani, and H. Dai, “A Computation Offloading Incentive Mechanism with Delay and Cost Constraints under 5G Satellite-ground IoV Architecture,” IEEE Wireless Communications, vol. 26, no. 4, pp. 124 – 132, Aug. 2019.
R. Zou and W. Wang, “U-CIMAN: Uncover Spectrum and User Information in LTE Mobile Access Networks,” IEEE INFOCOM 2020 – IEEE Conference on Computer Communications, Toronto, ON, Canada, 2020, pp. 1459-1468.
S.-C. Lin, “End-to-End Network Slicing for 5G&B Wireless Software-Defined Systems,” in Proc. of IEEE GLOBECOM, Abu Dhabi, UAE, December 2018.
Modulation and Multiple Access
Y. Yapici, I. Guvenc, and H. Dai, “Low-Resolution Limited-Feedback NOMA for mmWave Communications,” IEEE Trans. Wireless Communications, vol. 19, no. 8, pp. 5433 – 5446, Aug. 2020.
Y. Yapici and I. Guvenc, “NOMA for VLC Downlink Transmission With Random Receiver Orientation,” in IEEE Transactions on Communications, vol. 67, no. 8, pp. 5558-5573, Aug. 2019.
Compelling applications of cellular technology
V. Marojevic, I. Guvenc, R. Dutta, M. L. Sichitiu and B. A. Floyd, “Advanced Wireless for Unmanned Aerial Systems: 5G Standardization, Research Challenges, and AERPAW Architecture,” in IEEE Vehicular Technology Magazine, vol. 15, no. 2, pp. 22-30, June 2020.
J. Choi, V. Va, N. Gonzalez-Prelcic, R. Daniels, C. R. Bhat and R. W. Heath, “Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing,” in IEEE Communications Magazine, vol. 54, no. 12, pp. 160-167, December 2016.
W. Khawaja, O. Ozdemir, F. Erden, I. Guvenc and D. W. Matolak, “Ultra-Wideband Air-to-Ground Propagation Channel Characterization in an Open Area,” in IEEE Transactions on Aerospace and Electronic Systems.